Moores Cancer Center, UC San Diego, La Jolla, CA.
Department of Cellular and Molecular Medicine, UC San Diego, La Jolla, CA.
J Clin Oncol. 2024 Oct 20;42(30):3550-3560. doi: 10.1200/JCO.23.02641. Epub 2024 Jul 31.
Cancers with homologous recombination deficiency (HRD) can benefit from platinum salts and poly(ADP-ribose) polymerase inhibitors. Standard diagnostic tests for detecting HRD require molecular profiling, which is not universally available.
We trained DeepHRD, a deep learning platform for predicting HRD from hematoxylin and eosin (H&E)-stained histopathological slides, using primary breast (n = 1,008) and ovarian (n = 459) cancers from The Cancer Genome Atlas (TCGA). DeepHRD was compared with four standard HRD molecular tests using breast (n = 349) and ovarian (n = 141) cancers from multiple independent data sets, including platinum-treated clinical cohorts with RECIST progression-free survival (PFS), complete response (CR), and overall survival (OS) endpoints.
DeepHRD predicted HRD from held-out H&E-stained breast cancer slides in TCGA with an AUC of 0.81 (95% CI, 0.77 to 0.85). This performance was confirmed in two independent primary breast cancer cohorts (AUC, 0.76 [95% CI, 0.71 to 0.82]). In an external platinum-treated metastatic breast cancer cohort, samples predicted as HRD had higher complete CR (AUC, 0.76 [95% CI, 0.54 to 0.93]) with 3.7-fold increase in median PFS (14.4 3.9 months; = .0019) and hazard ratio (HR) of 0.45 ( = .0047). There were no significant differences in nonplatinum treatment outcome by predicted HRD status in three breast cancer cohorts, including CR (AUC, 0.39) and PFS (HR, 0.98, = .95) in taxane-treated metastatic breast cancer. Through transfer learning to high-grade serous ovarian cancer, DeepHRD-predicted HRD samples had better OS after first-line (HR, 0.46; = .030) and neoadjuvant (HR, 0.49; = .015) platinum therapy in two cohorts.
DeepHRD can predict HRD in breast and ovarian cancers directly from routine H&E slides across multiple external cohorts, slide scanners, and tissue fixation variables. When compared with molecular testing, DeepHRD classified 1.8- to 3.1-fold more patients with HRD, which exhibited better OS in high-grade serous ovarian cancer and platinum-specific PFS in metastatic breast cancer.
同源重组缺陷（HRD）的癌症可以从铂盐和聚（ADP-核糖）聚合酶抑制剂中获益。检测 HRD 的标准诊断测试需要分子分析，但并非普遍可用。
我们使用来自癌症基因组图谱（TCGA）的原发性乳腺癌（n=1008）和卵巢癌（n=459）训练了 DeepHRD，这是一种用于从苏木精和伊红（H&E）染色组织学幻灯片预测 HRD 的深度学习平台。使用来自多个独立数据集的乳腺癌（n=349）和卵巢癌（n=141）癌症，将 DeepHRD 与四种标准 HRD 分子测试进行了比较，包括具有 RECIST 无进展生存期（PFS）、完全缓解（CR）和总生存期（OS）终点的铂类治疗临床队列。
DeepHRD 预测 TCGA 中保留的 H&E 染色乳腺癌幻灯片中的 HRD，AUC 为 0.81（95%CI，0.77 至 0.85）。这一性能在两个独立的原发性乳腺癌队列中得到了证实（AUC，0.76 [95%CI，0.71 至 0.82]）。在一个外部的铂类治疗转移性乳腺癌队列中，预测为 HRD 的样本具有更高的完全 CR（AUC，0.76 [95%CI，0.54 至 0.93]），中位 PFS 增加了 3.7 倍（14.4 3.9 个月； =.0019），风险比（HR）为 0.45（ =.0047）。在三个乳腺癌队列中，根据预测的 HRD 状态，非铂类治疗结果没有显著差异，包括紫杉醇治疗转移性乳腺癌的 CR（AUC，0.39）和 PFS（HR，0.98， =.95）。通过转移学习到高级别浆液性卵巢癌，DeepHRD 预测的 HRD 样本在两个队列的一线（HR，0.46； =.030）和新辅助（HR，0.49； =.015）铂类治疗后具有更好的 OS。
DeepHRD 可以直接从多个外部队列、幻灯片扫描仪和组织固定变量的常规 H&E 幻灯片中预测乳腺癌和卵巢癌中的 HRD。与分子检测相比，DeepHRD 将 1.8 至 3.1 倍更多的 HRD 患者进行分类，在高级别浆液性卵巢癌中表现出更好的 OS，在转移性乳腺癌中表现出更好的铂类特异性 PFS。
